Sambamba: a runtime system for online adaptive parallelization

How can we exploit a microprocessor as efficiently as possible? The "classic" approach is static optimization at compile-time, optimizing a program for all possible uses. Further optimization can only be achieved by anticipating the actual usage profile: If we know, for instance, that two computations will be independent, we can run them in parallel. In the Sambamba project, we replace anticipation by adaptation. Our runtime system provides the infrastructure for implementing runtime adaptive and speculative transformations. We demonstrate our framework in the context of adaptive parallelization. We show the fully automatic parallelization of a small irregular C program in combination with our adaptive runtime system. The result is a parallel execution which adapts to the availability of idle system resources. In our example, this enables a 1.92 fold speedup on two cores while still preventing oversubscription of the system.